• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.405-408
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• 2008

Up to now, a lot of houses, roads and other urban facilities have been damaged by natural disasters such as flash floods and landslides. It is reported that the size and frequency of disasters are growing greatly due to global warming. In order to mitigate such disaster, flood forecasting and alerting systems have been developed for the Han river, Geum river, Nak-dong river and Young-san river. These systems, however, do not help small municipal departments cope with the threat of flood. In this study, a real-time urban flood forecasting service (U-FFS) is developed for ubiquitous computing city which includes small river basins. A test bed is deployed at Tan-cheon in Gyeonggido to verify U-FFS. It is found that U-FFS can forecast the water level of outlet of river basin and provide real-time data through internet during heavy rain. Furthermore, it is expected that U-FFS presented in this study can be applied to ubiquitous computing city (u-City) and/or other cities which have suffered from flood damage for a long time.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.2
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• pp.85-93
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• 2001

The basic concept of the model is to minimize the error range between forecasted flood inflow and actual flood inflow, and forecast accurately the flood discharge some hours in advance depending on the concentration time(Tc) and soil moisture retention storage(Sa). Simplex method that is a multi-level optimization technique was used to search for the determination of the best parameters of RETFLO (REal-Time FLOod forecasting) model. The flood forecasting model developed was applied to several strom event of Yedang reservoir during past 10 years. Model perfomance was very good with relative errors of 10% for comparison of total runoff volume and with one hour delayed peak time.

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.523-536
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• 2011

The objective of this study is to develop the data driven model for the flood forecasting that are improved the problems of the existing hydrological model for flood forecasting in medium and small streams. Neuro-Fuzzy flood forecasting model which linked the Takagi-Sugeno fuzzy inference theory with neural network, that can forecast flood only by using the rainfall and flood level and discharge data without using lots of physical data that are necessary in existing hydrological rainfall-runoff model is established. The accuracy of flood forecasting using this model is determined by temporal distribution and number of used rainfall and water level as input data. So first of all, the various combinations of input data were constructed by using rainfall and water level to select optimal input data combination for applying Neuro-Fuzzy flood forecasting model. The forecasting results of each combination are compared and optimal input data combination for real-time flood forecasting is determined.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.5
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• pp.81-89
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• 1995

One of the most difficult problem to estimate the flood inflow is how to understand the effective rainfall. The effective rainfall is absolutely influenced by the condition of soil moisture in the watershed just before the storm event. DAWAST model developed to simulate the daily streamflow considering the meteologic and geographic characteristics in the Korean watersheds was applied to understand the soil moisture and estimate the effective rainfall rather accurately through the daily water balance in the watershed. From this soil moisture and effective rainfall, concentration time, dimensionless hydrograph, and addition of baseflow, the rainfall-runoff model for flood flow was developed by converting the concept of long-term runoff into short-term runoff. And, real-time flood forecasting model was also developed to forecast the flood-inflow hydrograph to the river and reservoir, and called RETFLO model. According to the model verification, RETFLO model can be practically applied to the medium and small river and reservoir to forecast the flood hydrograph with peak discharge, peak time, and volume. Consequently, flood forecasting and warning system in the river and the reservoir can be greatly improved by using personal computer.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.295-299
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• 2001

To protect the flooding damages in Medium and Small watershed, it needs to set up flood warning system and develope Flood forecasting Model in real-time basis for medium and small watershed. In this study, it was able to minimize the error range between forecasted flood inflow and actual flood inflow, and forecast accurately the flood discharge some hours in advance by using simplex method recursively for the determination of the best parameters of RETFLO model. The result of RETFLO performance applied to several storm of Yugu river during 3 past years was very good with relative errors of 10% for comparison of total runoff volume and with one hour delayed peak time.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.285-288
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• 2002

For real-time flood forecasting and effective control flood at the Youngsan estuary dam, the Flood Forecasting and Control User Interface System II (FFCUS II) has been developed. This paper describes the features and application of FFCUS II. FFCUS II is composed of the database management subsystem, the model subsystem, and the graphic user interface. The database management subsyem collects rainfall data and stream flow data, updates, processes, and searches the data. The model subsystem predicts the inflow hydrograph, the tide, forecasts flood hydrograph, and simulates the release rate from the sluice gates. The graphic user interface subsystem aids the user's decision-making process by displaying the operation results of the database management subsystem and model subsystem.

• Water for future
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• v.27 no.1
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• pp.89-99
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• 1994

Flood forecasting in Korea has been based on the off-line parameter estimation method. But recent flood forecasting studies explore on-line recursive parameter estimation algorithms. In this study, a simultaneous adaptive estimation of system states and parameters for rainfall-runoff model is investigated for on-line real-time flood forecasting and parameter estimation. The proposed flood routing system is composed of Flood forecasting in Korea has been based on the off-line parameter estimation method. But recent flood forecasting studies explore on-line recursive parameter estimation algorithms. In this study, a simultaneous adaptive estimation of system states and parameters for rainfall-runoff model is investigated for on-line real-time flood forecasting and parameter estimation. The proposed flood routing system is composed of ø-index in the assessment of effective rainfall and the cascade of nonlinear reservoirs accounting for translation effect in flood routing. To combine the flood routing model with a parameter estimation model, system states and parameters are treated with the extended state-space formulation. Generalized least squares and maximum a posterior estimation algorithms are comparatively examined as estimation techniques for the state-space model. The sensitivity analysis is to investigate the identifiability of the parameters. The index of sensitivity used in this study is the covariance matrix of the estimated parameters.-index in the assessment of effective rainfall and the cascade of nonlinear reservoirs accounting for translation effect in flood routing. To combine the flood routing model with a parameter estimation model, system states and parameters are treated with the extended state-space formulation. Generalized least squares and maximum a posterior estimation algorithms are comparatively examined as estimation techniques for the state-space model. The sensitivity analysis is to investigate the identifiability of the parameters. The index of sensitivity used in this study is the covariance matrix of the estimated parameters.

• Journal of Korea Water Resources Association
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• v.43 no.1
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• pp.51-65
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• 2010

We suggest a simple and practical flood forecasting and warning system, which can predict change in the water level of a river in a small to medium-size watershed where flash flooding occurs in a short time. We first choose the flood defense target points, through evaluation of the flood risk of dike overflow and lowland inundation. Using data on rainfall, and on the water levels at the observed and prediction points, we investigate the interrelations and derive a regression formula from which we can predict the flood level at the target points. We calculate flood water levels through a calibrated flood simulation model for various rainfall scenarios, to overcome the shortage of real water stage data, and these results as basic population data are used to derive a regression formula. The values calculated from the regression formula are modified by the weather condition factor, and the system can finally predict the flood stages at the target points for every leading time. We also investigate the applicability of the prediction procedure for real flood events of the Jungnang Stream basin, and find the forecasting values to have close agreement with the surveyed data. We therefore expect that this suggested warning scheme could contribute usefully to the setting up of a flood forecasting and warning system for a small to medium-size river basin.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.437-442
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• 2005

The forecasting of flood runoff in the river is essential for flood control. The purpose of this study is to test a development of system for flood runoff forecasting using neural network model. For the flood events the tested rainfall and runoff data were the input to the input layer and the flood runoff data were used in the output layer To choose the forecasting model which would make up of runoff forecasting system properly, real-time runoff in the river when flood periods were forecasted by using the neural network model and the state-space model. A comparison of the results obtained by the two forecasting models indicated the superiority and reliability of the neural network model over the state-space model. The neural network model was modified to work in the Web and developed to be the basic model of the forecasting system for the flood runoff.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.